The Consideration of Indigenous Peoples in High Stakes Evaluations of Risk
Bibliographic record
Abstract
While Indigenous peoples account for a small portion of the Canadian population, they are overrepresented in the Canadian Criminal Justice System. Research and case law suggest culture should always be considered in violence risk assessments (VRAs), but it is unknown whether this recommendation is followed. The present study examined the role of Indigenous versus non-Indigenous culture in judicial opinions regarding evaluators’ VRA and expert witness testimony in Dangerous Offender and Long-Term Offender (DO/LTO) hearings under Canadian Law. 214 DO/LTO hearings from 2009-2016 where judges commented on VRAs submitted to the court were systematically identified via the Canadian Legal Information Institute database. Judicial comments were analyzed in cases with Indigenous and non-Indigenous defendants for discussions of culture and the prevalence of comments regarding qualities of the evaluator(s), qualities of the VRA(s) completed, and qualities of the evaluators’ expert testimony about the VRA. Judges considered culture meaningfully in 64% of Indigenous offenders’ cases. Discussion of VRA tools’ content was significantly more frequent in non-Indigenous cases; otherwise, frequency of non-cultural themes did not vary between case groups. Given the importance of considering culture in VRA, it is concerning that culture was considered in just over half of cases; improving this deficit is discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".